H
Hong-Linh Truong
Researcher at Aalto University
Publications - 234
Citations - 5035
Hong-Linh Truong is an academic researcher from Aalto University. The author has contributed to research in topics: Cloud computing & Web service. The author has an hindex of 34, co-authored 225 publications receiving 4614 citations. Previous affiliations of Hong-Linh Truong include University of Vienna & University of Innsbruck.
Papers
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Proceedings ArticleDOI
Service-centric Inference and Utilization of Confidence on Context
TL;DR: This paper presents a novel technique to combine different Quality of Context (QoC) metrics to infer the value of confidence on context and provides advice to context consumers to select high quality context and use the confidence in their functionality.
Book ChapterDOI
QUELLE – A Framework for Accelerating the Development of Elastic Systems
TL;DR: QUELLE is introduced – a framework for evaluating and recommending SES deployment configurations from cloud services that both provide the required elasticity, and fulfill cost, quality, and resource requirements, and thus can be incorporated into different phases of the development of SESs.
Proceedings ArticleDOI
Analyzing Reliability in Hybrid Compute Units
TL;DR: This paper presents models and a framework for analyzing the reliability of hybrid compute units (HCU), which represent on-demand collectives of humans collaboration supported by machines (hardware and software units) for performing tasks.
Book ChapterDOI
Augmenting Complex Problem Solving with Hybrid Compute Units
TL;DR: Novel methods for modeling and developing hybrid compute units of software-based and human-based services and high-level programming elements reflecting hybridity, collectiveness and adaptiveness properties, and on-demand and pay-per-use economic properties, for complex problem solving are presented.
Book ChapterDOI
DRain: An Engine for Quality-of-Result Driven Process-Based Data Analytics
TL;DR: The fundamental building blocks of a framework for enabling process selection and configuration through user-defined QoR at runtime are presented and these building blocks form the basis to support modeling, execution and configuration of data-aware process variants in order to perform analytics.